This paper proposes a novel adaptive optimization algorithm to solve the network reconfiguration and distributed generation (DG) placement problems with objective functions including power loss minimization and voltage stability index (VSI) improvement. The proposed technique called Adaptive Shuffled Frogs Leaping Algorithm (ASFLA) was performed for solving network reconfiguration and DG installation in IEEE 33- and 69-bus distribution systems with seven different scenarios. The performance of ASFLA was compared to that of other algorithms such as Fireworks Algorithm (FWA), Adaptive Cuckoo Search Algorithm (ACSA) and Shuffled Frogs Leaping Algorithm (SFLA). It was found that the power loss and VSI provided by ASFLA were better than those given by FWA, ACSA and SFLA in both 33- and 69-bus systems. The best solution of power loss reduction and VSI improvement of both 33- and 69-bus systems was achieved when the network reconfiguration with optimal sizing and the location DG were simultaneously implemented. From our analysis, it was indicated that the ASFLA could provide better solutions than other methods since the generating process, local and global searching of this algorithm were significantly improved from a conventional method. Hence, the ASFLA becomes another effective algorithm for solving network reconfiguration and DG placement problems in electrical distribution systems.

Power quality disturbance (PQD) is an important issue in electrical distribution systems that needs to be detected promptly and identified to prevent the degradation of system reliability. This work proposes a PQD classification using a novel algorithm, comprised of the artificial bee colony (ABC) and the particle swarm optimization (PSO) algorithms, called “adaptive ABC-PSO” as the feature selection algorithm. The proposed adaptive technique is applied to a combination of ABC and PSO algorithms, and then used as the feature selection algorithm. A discrete wavelet transform is used as the feature extraction method, and a probabilistic neural network is used as the classifier. We found that the highest classification accuracy (99.31%) could be achieved through nine optimally selected features out of all 72 extracted features. Moreover, the proposed PQD classification system demonstrated high performance in a noisy environment, as well as the real distribution system. When comparing the presented PQD classification system’s performance to previous studies, PQD classification accuracy using adaptive ABC-PSO as the optimal feature selection algorithm is considered to be at a high-range scale; therefore, the adaptive ABC-PSO algorithm can be used to classify the PQD in a practical electrical distribution system.

The rotor permanent magnet flux-switching (RPM-FS) machine is a promising candidate for electric vehicle (EV) and hybrid electric vehicle (HEV) applications. In this paper, we propose the magnetic flux barrier design to improve the torque capability of the RPM-FS machine. The response surface optimization method was used to design and optimize the topology of flux barriers. The 2D finite element analysis shows that the proposed RPM-FS machine has a higher electromotive force than the conventional structure, with only a slight increase in cogging torque. Notably, an insertion of flux barriers could yield a reduction of magnetic flux leakage, an improvement of magnetic saturation capability, and an enhancement of working harmonics of the air-gap flux density. As a result, a significant improvement in torque capability, eddy current losses, and efficiency was obtained. Hence, the RPM-FS machine proposed in this work is capable of being used in EV and HEV applications.

Interior permanent magnet (IPM) motors have garnered widespread popularity in electric vehicle (EV) applications due to their distinctive advantages, including superior torque density, high efficiency, and a wide speed range. In this work, the asymmetric hybrid-layer del-shaped (∇-shaped) IPM motor is introduced to enhance the torque capability of the benchmark IPM motor designed for EVs. The proposed motor incorporated an asymmetric PM configuration, integrating hybrid-layer PMs arranged in a ∇-shaped pattern. To achieve the design objectives, the initial motor design was optimized using a Genetic algorithm, considering sensitivity analysis to precisely attain the targeted torque capability based on EV-specific objective functions. Subsequently, the electromagnetic performance of the proposed motor was analyzed through finite element analysis, and a comparison was made with a symmetric IPM benchmark structure. The results clearly demonstrate the superiority of the proposed asymmetric IPM motor, which exhibits significantly higher average torque, attributed to the effective utilization of the magnetic field shifting effect. Furthermore, a notable reduction in torque ripple was revealed, with this motor experiencing only 9.62% torque ripple, rendering it a suitable choice for EV applications. Additionally, enhanced magnet utilization resulting from the asymmetric permanent magnet configuration in the proposed motor was observed. The overall efficiency of the asymmetric IPM motor was enhanced, reaching an impressive 96.68%. Consequently, the proposed asymmetric IPM motor represents an eminently practical and efficient solution for EV applications.

This work aims to maximize the benefit of the low-voltage (LV) level distribution system with high photovoltaic (PV) penetration by using an optimal installation of a battery energy storage system (BESS) and capacitor. The 41-bus practical distribution system located in Thailand was focused on. The comprehensive objective function regarding the focused system was proposed. The Salp Swarm and Genetic Algorithms were applied to solve the optimization problem. The total net present value (NPV) of utility was performed as a beneficial indicator, and it was determined by the overall costs and benefits of BESS installation and capacitor placement. A comparison of total NPV in the cases of centralized BESS installation, BESS installation with LV capacitor placement, and decentralized BESS installation was indicated. The results showed that all cases of BESS installation could increasingly flatten the load on the transformer; meanwhile, the voltage profile of the system was significantly improved. Optimal installation of centralized BESS simultaneously with LV capacitor placement provides higher NPV than the case with only centralized BESS installation. In particular, the highest NPV was obtained in the case of installing decentralized BESS. The results can be utilized to maximize the benefits of the utility in the distribution system at a high PV penetration level.

The double-sided axial flux (TORUS) permanent magnet (PM) machine has been shown to be an efficient structure for electric vehicles (EVs). This paper introduces the combined techniques of skewed PMs and Halbach-array arrangement to improve the TORUS-type axial flux PM motor performance. The suitable configuration for implementing this combination in the TORUS PM machine was investigated, considering two possible models. Each model of the skewed Halbach-array PM TORUS motor is optimized using response surface methodology to meet the performance requirements of an EV application. A comparative evaluation of machine performance via 3D finite element analysis is performed and detailed insight into the design and configuration of the skewed Halbach-array PM TORUS machine is presented. Under no-load conditions, the optimal skewed Halbach-array PM TORUS motor exhibits 4% higher back-EMF and 9.3% lower cogging torque compared to a benchmark TORUS motor. During on-load operation, the proposed motor shows an 8% increase in average torque compared to the benchmark, accompanied by a significant 7.8% reduction in torque ripple. The rationale behind this enhanced torque capability is explained in detail and is found to be due to enhanced flux-concentration and flux-cancellation effects within the motor. The efficiency of the proposed TORUS motor is enhanced. The results confirm the superiority of the combined skewed Halbach PM Array techniques to improve TORUS PM motor performance and suggest suitable configurations for this combined technique in EV motors.